TECHNOLOGY AREAS: Air Platform; Information Systems; Nuclear; Electronics; Materials; Weapons OBJECTIVE: The end state we aim to achieve with this Phase I STTR project centers on developing a foundational AI-driven toolchain specifically tailored for enhancing the operational resilience of the Air Force and Space Force through superior supply chain analytics. This toolchain will incorporate cutting-edge artificial intelligence to seamlessly integrate with existing defense supply chain frameworks, significantly enhancing real-time decision-making capabilities. Our objective is to provide a robust proof of concept that demonstrates the feasibility of translating complex supply chain data into actionable insights through an intuitive user interface designed for logistics subject matter experts (SMEs). This will be achieved by leveraging advanced machine learning techniques, including Retrieval-Augmented Generation (RAG), to ensure the reliability and accuracy of data outputs. Moreover, the project will establish a secure, scalable architecture that integrates with the Air Force Research Laboratory's (AFRL) Earth 616 program's existing graph databases and blockchain infrastructure. This integration aims to enhance the transparency and security of data processes within the Defense Industrial Base (DIB), setting the stage for Phase II development and eventual deployment. Ultimately, the success of this project will be measured by its ability to reduce the reliance on large data analytics teams, thereby speeding up the response times to critical supply chain challenges and allowing for a more agile adaptation to dynamic operational environments. By the project's conclusion, we intend to have laid the groundwork for a transformative tool that not only meets the immediate needs of the Air Force and Space Force but also has significant potential for broader commercial application within complex supply chain systems across various sectors. DESCRIPTION: To meet the objectives of this Phase I STTR project, which involves initial research into developing an AI-driven toolchain for improving supply chain analytics within the Defense Industrial Base (DIB), the following work plan is suggested. Preliminary Data Architecture Development: Objective: Establish a basic framework for data integration that supports AI capabilities, aligning with the Earth 616 program's existing technologies. Approach: Conduct a feasibility study to identify the optimal data structures and integration points between AI models and existing DIB databases, focusing on scalability and security for future phases. AI Model Conceptualization: Objective: Design preliminary AI models that utilize large language models (LLMs) and Retrieval-Augmented Generation (RAG) to interpret and respond to logistics queries. Approach: Develop initial AI prototypes that demonstrate basic understanding and processing of supply chain data. This includes setting up the groundwork for advanced AI development in Phase II by exploring different machine learning frameworks and algorithms. User Interface (UI) Prototyping: Objective: Design a simple, intuitive user interface prototype that enables direct interaction of logistics SMEs with the AI toolchain. Approach: Sketch the initial design of the UI that facilitates easy input of queries and displays AI-generated insights in a clear, understandable manner. This task will involve basic mock-ups and flow diagrams to outline the user interaction without fully implementing the interface. Initial Security Framework Setup: Objective: Outline a security strategy to protect data integrity and comply with military standards, focusing on blockchain integrations. Approach: Identify key security requirements and potential technologies that can be incorporated into the toolchain. Develop a basic security plan that includes data encryption and access controls suitable for a research prototype. Exploratory Testing and Validation: Objective: Conduct initial testing of the AI prototypes and data architecture to validate their potential for Phase II expansion. Approach: Implement rudimentary tests to evaluate the conceptual models and frameworks for functionality and feasibility. This early-stage testing will focus on identifying major flaws or limitations that could impede further development. Documentation and Reporting: Objective: Produce detailed documentation of the research findings, methodologies, and initial prototypes developed during Phase I. Approach: Compile a comprehensive report that includes technical descriptions, test results, and conceptual plans for the next phase of development. This document will serve as a foundational resource for scaling the project. This Phase I effort is designed to establish the viability and strategic direction for a more detailed and resource-intensive development in Phase II, ensuring that the groundwork is laid for a transformative tool that meets the future needs of the Air Force and Space Force. PHASE I: The objective of this STTR Phase I project is to create an AI-driven toolchain to enhance decision-making within the Defense Industrial Base (DIB) by simplifying complex data analysis for the Air Force and Space Force. The project will develop a proof of concept demonstrating the feasibility of using AI to operationalize data analytics and improve operational readiness. Background: Building upon the successes of the Earth 616 program, this project seeks to further integrate and leverage AI and machine learning technologies to address the complexities of modern military supply chains. The Earth 616 program demonstrated the effectiveness of using advanced data architectures like graph databases and blockchain for enhancing data visibility and integrity. This project will expand on those technologies to create an intuitive, accessible interface for non-technical personnel. Scope of Work: Data Architecture Development: Design and develop a preliminary data architecture that integrates seamlessly with existing DIB systems, focusing on scalability and security. AI Model Development: Prototype AI models that leverage natural language processing and machine learning to transform complex logistics data into understandable insights. Interface Design and Usability Testing: Design a user-friendly interface that allows straightforward interaction via natural language queries and conduct usability testing with target end-users to ensure the interface meets their needs. Feasibility Testing and Validation: Execute initial tests to validate the AI toolchain across predefined use cases, which include scenarios of logistical disruptions and supply chain anomalies. Expected Outcomes: By the end of Phase I, we expect to have a validated proof of concept that: Demonstrates the AI toolchain's ability to effectively interpret and analyze complex supply chain data. Showcases a secure, user-friendly interface that simplifies user interaction and accelerates decision-making processes. Provides initial performance metrics and feedback from potential end-users within the military logistics community. Future Work: Phase II will expand on the initial models and framework developed in Phase I, enhancing the toolchain's capabilities and integrating additional data sources for greater predictive accuracy. It will also involve broader testing with end-users to refine the toolchain and ensure it meets operational needs across different military environments. The ultimate goal is to deploy a fully operational system that can be used across the Air Force to bolster supply chain resilience and decision support. Period of Performance Objectives and Expectations: During the Phase I period, the project will focus on establishing technical feasibility through: Development of a Basic AI Model: Establishing baseline functionality that can handle routine supply chain queries. Prototype Interface Development: Ensuring the interface is intuitive and meets the basic needs of logistics personnel. Initial Use Case Validation: Using specific scenarios such as sudden demand spikes or logistics route disruptions to test the model's responsiveness and accuracy. Operational Parameters Setup: Defining and documenting the operational parameters under which the AI toolchain will be developed and tested, ensuring alignment with DIB operational standards and requirements. PHASE II: The objective of the Phase II effort will be to develop and validate an advanced Generative Artificial Intelligence (GenAI) toolchain designed to integrate seamlessly with the Earth 616 program. By leveraging the unique datasets associated with the KC-46 program, alongside Air Force and Defense Logistics Agency (DLA) Enterprise data sources, this effort aims to enhance decision-making within the Defense Industrial Base (DIB). The goal is to provide sophisticated, real-time analytics that directly support logistics and supply chain subject matter experts (SMEs), ultimately improving operational readiness and resilience. Prototyping Expectations: Prototyping in Phase II will focus on developing a GenAI toolchain that operates in concert with existing Earth 616 infrastructure. Key aspects of the toolchain will include: Seamless Integration with Earth 616: The GenAI toolchain will be designed to integrate with Earth 616's graph databases and blockchain-based datastores. This integration is intended to ensure secure and context-aware data management, enabling SMEs to make informed decisions based on the most current and relevant data. Customization for Specific Data Sources: The toolchain will be tailored to effectively utilize data from the KC-46 program and other relevant Air Force and DLA Enterprise datasets, demonstrating its applicability and value in real-world logistics environments. Operating Parameters: The toolchain will be developed to operate within defined parameters to ensure it meets the requirements of existing Air Force systems, while also being adaptable to future needs: Interoperability with Earth 616: The toolchain must operate seamlessly within Earth 616's distributed ledger technology (DLT) and existing database architectures, ensuring smooth integration into current operations. Data Handling: It will be capable of processing and analyzing specific data types and formats used within KC-46 and DLA logistics environments, with an emphasis on ensuring data integrity and security. Compliance: The toolchain will adhere to all applicable military cybersecurity and data handling standards, ensuring it is fully compliant with DoD requirements. Testing Requirements: To validate the effectiveness of the toolchain, a comprehensive testing strategy will be implemented: Functional Testing: This will ensure the toolchain integrates correctly with Earth 616 systems and manages data flows efficiently and securely. Performance Testing: The toolchain's ability to process and analyze logistics data from the KC-46 program will be evaluated, with a focus on efficiency, accuracy, and relevance. User Acceptance Testing: SMEs from logistics and supply chain sectors will participate in user acceptance testing to refine the toolchain's usability and functionality, ensuring it meets operational needs. Success Criteria: Success in Phase II will be measured by the toolchain's ability to: Enhance Decision-Making: The toolchain should provide measurable improvements in the accuracy, relevance, and timeliness of logistics decisions, helping SMEs make more informed and effective decisions. Demonstrate Operational Value: The toolchain will be evaluated on its ability to improve overall operational efficiency and resilience, particularly in scenarios requiring rapid decision-making and response. Achieve User Satisfaction: The toolchain's usability and functionality will be assessed through user feedback, with an emphasis on ensuring it meets the needs of SMEs and other key stakeholders. Future Work: Upon successful validation in Phase II, the toolchain may be expanded in Phase III to support broader applications across additional Air Force and DLA programs. The focus will be on full operational deployment, with further development aimed at enhancing the toolchain's predictive and prescriptive analytics capabilities, ensuring it continues to provide valuable support for military logistics and supply chain management. PHASE III DUAL USE APPLICATIONS: Objective and Scope: Phase III of this project will focus on transitioning the Generative Artificial Intelligence (GenAI) toolchain developed in Phase II into full operational use within both the Department of Defense (DoD) environments, particularly the Air Force and Space Force, and exploring potential commercial applications. The primary goal is to ensure that the toolchain can be seamlessly integrated into existing military operations and civilian sectors that benefit from advanced supply chain analytics. Expected Technology Readiness Level (TRL) at Phase III Entry: The GenAI toolchain is expected to enter Phase III with a Technology Readiness Level (TRL) of 6. This indicates that the technology will have demonstrated a high fidelity prototype in a relevant environment (i.e., within the Earth 616 framework using real Air Force data). The Phase II activities are designed to validate the toolchain's functionalities, ensuring it meets the operational needs and integration requirements for broader deployment. Transition Planning: The transition plan for Phase III includes several key components: 1. Operational Deployment: Collaborate with Air Force Program Executive Offices (PEOs) and Systems Program Offices (SPOs) to facilitate the deployment of the toolchain across various Air Force supply chain operations. This includes integration with existing logistics and data analytics platforms used by the Air Force and Space Force. 2. Training and Support: Develop comprehensive training programs for end-users to ensure they can effectively utilize the GenAI toolchain. This will include both online and in-person training modules, focusing on how to use the toolchain for enhancing decision-making in supply chain management. 3. Continuous Improvement and Updates: Establish a feedback loop with end-users to continuously improve the toolchain functionalities based on real-world use and evolving needs. This will involve regular updates and patches to the software to address any emerging challenges and incorporate new data sources as they become available. 4. Commercial Sector Transition: Identify and engage with commercial partners in industries such as manufacturing and logistics that could benefit from enhanced predictive analytics and data visualization capabilities. The toolchain will be adapted to meet the specific needs of these industries, ensuring it provides value in both military and civilian contexts. 5. Compliance and Certification: Ensure all necessary compliance with DoD cybersecurity standards and commercial data protection regulations. This will include obtaining any necessary certifications to deploy the technology in sensitive or regulated environments. Expected Outcomes: By the end of Phase III, the GenAI toolchain is expected to be fully operational within the DoD and beginning to gain traction in commercial sectors. It should demonstrate significant improvements in supply chain decision-making accuracy, efficiency, and responsiveness, contributing to enhanced operational readiness and reduced costs. Future Work: Looking beyond Phase III, future work will focus on expanding the toolchain's capabilities to incorporate emerging AI technologies and data analytics methodologies. This will include exploring the use of machine learning models for predictive maintenance and the integration of Internet of Things (IoT) data to further enhance supply chain visibility and resilience. REFERENCES: 1. Ivanov, D., Dolgui, A., Sokolov, B. (2019). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research. https://doi.org/10.1007/s10479-019-03379-4 2. Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. International Journal of Logistics Research and Applications, 13(1), 13-39. https://doi.org/10.1080/13675560902736536 3. Wang, Y., Kung, L., Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13. https://doi.org/10.1016/j.techfore.2015.12.019 KEYWORDS: Artificial Intelligence; Machine Learning; Supply Chain Logistics; Knowledge Graphs; Scenario Generation; Data Analytics; Distributed Ledger Technology; Blockchain; Operational Readiness; Military Logistics; Data Integration; AI-Enabled Decision Support; Resilient Supply Chains; Data Security; User Interface Design
